Beginning R · an Introduction to Statistical Programming by Pace, Larry -- Read -- Imperial Library of Trantor

Index

Cover Title Copyright Dedication Contents at a Glance Contents About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1: Getting R and Getting Started

Getting and Using R A First R Session Moving Around in R Working with Data in R

Vectors Matrices Lists Data Frames

Dealing With Missing Data in R Conclusion

Chapter 2: Programming in R

What is Programming? Getting Ready to Program The Requirements for Learning to Program Flow Control

Looping Conditional Statements and Branching

Essentials of R Programming

R Operators Input and Output in R

Understanding the R Environment Implementation of Program Flow in R

For Loops While and Repeat Loops Avoiding Explicit Loops: The Apply Function Family

A First R Program Another Example—Finding Pythagorean Triples Using R to Solve Quadratic Equations Why R is Object-Oriented

The S3 and S4 Classes Generic Functions

Conclusion

Chapter 3: Writing Reusable Functions

Examining an R Function from the Base R Code Creating a Function Calculating a Confidence Interval for a Mean Avoiding Loops with Vectorized Operations Vectorizing If-Else Statements Using ifelse() Making More Powerful Functions Any, All, and Which Making Functions More Useful Confidence Intervals Revisited Conclusion

Chapter 4: Summary Statistics

Measuring Central Tendency

The Mean The Median and Other Quantiles The Mode

Measuring Location via Standard Scores Measuring Variability

Variance and Standard Deviation Range Median and Mean Absolute Deviation The Interquartile Range The Coefficient of Variation

Covariance and Correlation Measuring Symmetry (or Lack Thereof) Conclusion

Chapter 5: Creating Tables and Graphs

Frequency Distributions and Tables Pie Charts and Bar Charts

Pie Charts Bar Charts

Boxplots Histograms Line Graphs Scatterplots Saving and Using Graphics Conclusion

Chapter 6: Discrete Probability Distributions

Discrete Probability Distributions Bernoulli Processes

The Binomial Distribution: The Number of Successes as a Random Variable The Poisson Distribution

Relating Discrete Probability to Normal Probability Conclusion

Chapter 7: Computing Normal Probabilities

Characteristics of the Normal Distribution

Finding Normal Densities Using the dnorm Function Converting a Normal Distribution to the Standard Normal Distribution Finding Probabilities Using the pnorm Function Finding Critical Values Using the qnorm Function Using rnorm to Generate Random Samples

The Sampling Distribution of Means A One-sample z Test Conclusion

Chapter 8: Creating Confidence Intervals

Confidence Intervals for Means

Confidence Intervals for the Mean Using the Normal Distribution Confidence Intervals for the Mean Using the t Distribution

Confidence Intervals for Proportions Understanding the Chi-square Distribution Confidence Intervals for Variances and Standard Deviations Confidence Intervals for Differences between Means Confidence Intervals Using the stats Package Conclusion

Chapter 9: Performing t Tests

A Brief Introduction to Hypothesis Testing Understanding the t Distribution The One-sample t Test The Paired-samples t Test Two-sample t Tests

The Welch t Test The t Test Assuming Equality of Variance

A Note on Effect Size for the t Test Conclusion

Chapter 10: One-Way Analysis of Variance

Understanding the F Distribution Using the F Distribution to Test Variances Compounding Alpha and Post Hoc Comparisons One-Way ANOVA

The Variance Partition in the One-Way ANOVA An Example of the One-Way ANOVA Tukey HSD Test Bonferroni-Corrected Post Hoc Comparisons

Using the anova Function Conclusion

Chapter 11: Advanced Analysis of Variance

Two-Way ANOVA

Sums of Squares in Two-Way ANOVA An Example of a Two-Way ANOVA Examining Interactions Plotting a Significant Interaction Effect Size in the Two-Way ANOVA

Repeated-Measures ANOVA

The Variance Partition in Repeated-Measures ANOVA Example of a Repeated-Measures ANOVA Effect Size for Repeated-Measures ANOVA

Mixed-Factorial ANOVA

Example of a Mixed-Factorial ANOVA

Conclusion

Chapter 12: Correlation and Regression

Covariance and Correlation Regression An Example: Predicting the Price of Gasoline

Examining the Linear Relationship Fitting a Quadratic Model

Determining Confidence and Prediction Intervals Conclusion

Chapter 13: Multiple Regression

The Multiple Regression Equation Multiple Regression Example: Predicting Job Satisfaction Using Matrix Algebra to Solve a Regression Equation Brief Introduction to the General Linear Model

The t Test as a Special Case of Correlation The t Test as a Special Case of ANOVA ANOVA as a Special Case of Multiple Regression